It's been instructive to see "Ballerina," which opens this week, so soon after the new "Mission: Impossible" installment. In the latter, it's hard to top Tom Cruise's intrepid stunt work, which reaches its zenith in a pair of extended sequences (one in a submarine, the other on biplanes), but the story, involving a diabolical scheme using A.I. to commandeer and launch the world's nuclear weaponry, is a mere pretext. Going to "Mission: Impossible" for the story is like going to Casablanca for the waters. In contrast, "Ballerina"--like the four John Wick films that it's spun off from--is, strangely, far better at story than at action. The first John Wick film is the weakest, because the framework for the franchise was still unformed: a retired hit man (Keanu Reeves) gets back into action to respond to a mobster's attacks.

In this paper, we explore the relevance of large language models (LLMs) for annotating references to Roman and Greek mythological entities in modern and contemporary French literature. We present an annotation scheme and demonstrate that recent LLMs can be directly applied to follow this scheme effectively, although not without occasionally making significant analytical errors. Additionally, we show that LLMs (and, more specifically, ChatGPT) are capable of offering interpretative insights into the use of mythological references by literary authors. However, we also find that LLMs struggle to accurately identify relevant passages in novels (when used as an information retrieval engine), often hallucinating and generating fabricated examples--an issue that raises significant ethical concerns. Nonetheless, when used carefully, LLMs remain valuable tools for performing annotations with high accuracy, especially for tasks that would be difficult to annotate comprehensively on a large scale through manual methods alone.

We introduce a novel approach to enhance the capabilities of text-to-image models by incorporating a graph-based RAG. Our system dynamically retrieves detailed character information and relational data from the knowledge graph, enabling the generation of visually accurate and contextually rich images. This capability significantly improves upon the limitations of existing T2I models, which often struggle with the accurate depiction of complex or culturally specific subjects due to dataset constraints. Furthermore, we propose a novel self-correcting mechanism for text-to-image models to ensure consistency and fidelity in visual outputs, leveraging the rich context from the graph to guide corrections. Our qualitative and quantitative experiments demonstrate that Context Canvas significantly enhances the capabilities of popular models such as Flux, Stable Diffusion, and DALL-E, and improves the functionality of ControlNet for fine-grained image editing tasks. To our knowledge, Context Canvas represents the first application of graph-based RAG in enhancing T2I models, representing a significant advancement for producing high-fidelity, context-aware multi-faceted images.

Recent works in open-domain question answering (QA) have explored generating context passages from large language models (LLMs), replacing the traditional retrieval step in the QA pipeline. However, it is not well understood why generated passages can be more effective than retrieved ones. This study revisits the conventional formulation of QA and introduces the concept of knowledge corpus error. This error arises when the knowledge corpus used for retrieval is only a subset of the entire string space, potentially excluding more helpful passages that exist outside the corpus. LLMs may mitigate this shortcoming by generating passages in a larger space. We come up with an experiment of paraphrasing human-annotated gold context using LLMs to observe knowledge corpus error empirically. Our results across three QA benchmarks reveal an increased performance (10% - 13%) when using paraphrased passage, indicating a signal for the existence of knowledge corpus error. Our code is available at https://github.com/xfactlab/emnlp2023-knowledge-corpus-error

Over the past several weeks, there has been a wave of reactions to recent developments in the world of Ai and the arts, which seems pretty well typified in this article from the Verge. I'd like to get into why I think this line of thought is asking the wrong questions for the wrong reasons, but you'll have to bear with me a little. Let me start with a little about me. I'm a "multi-hyphenate" artist, which is to say I've done a lot of work in a variety of mediums: music production, visual art, writing. I'm not famous, but I've developed a methodology already, and didn't have much interest in the early Ai apps of yesteryear, until one came along that seemed to present some utility as tool rather than replacement. In talking to several artist friends of mine, I heard that MidJourney had an approach to Licensing that seemed to have artists in mind.

The Dark is the Wasteland's underworld; the deepest underworld of all. Keyword, The wasteland is where a group of Wild Snarks live; they have rebelled against the humans. With them lives Alice; Countess Alice, the blue and white rabbits. Its under world (not heaven or hell) is called the dark. The main city of the dark is called Dystopia, a truly dystopian city.

Wild Snark and Scorpion Designs are brand names of Martin Wall. He is a mathematical physicists and digital artists that lives in the united kingdom. His artwork tends towards conceptual art. He specialises in AI art;AI stands for artificial intelligence and involves both machine learning and deep learning. These and vr (virtual reality), ar (augmented reality) and design think form a key elements of the metaverse.

The Dark is the Wasteland's underworld; the deepest underworld of all. Keyword, The wasteland is where a group of Wild Snarks live; they have rebelled against the humans. With them lives Alice; Countess Alice, the blue and white rabbits. Its under world (not heaven or hell) is called the dark. The main city of the dark is called Dystopia, a truly dystopian city.

I Machine explore the human machine interface as well as body dysphoria. The Dark is the Wasteland's underworld; the deepest underworld of all. Keyword, The wasteland is where a group of Wild Snarks live; they have rebelled against the humans. With them lives Alice; Countess Alice, the blue and white rabbits. Its under world (not heaven or hell) is called the dark.

Wild Snark and Scorpion Designs are brand names of Martin Wall. He is a mathematical physicists and digital artists that lives in the united kingdom. His artwork tends towards conceptual art. He specialises in AI art;AI stands for artificial intelligence and involves both machine learning and deep learning. These and vr (virtual reality), ar (augmented reality) and design think form a key elements of the metaverse.